Multi Criterion Recommendation

Multi-criteria recommendation systems aim to improve recommendation accuracy by considering multiple user preferences and item attributes, going beyond simple ratings. Current research focuses on hybrid models combining collaborative and content-based filtering, often incorporating advanced techniques like genetic algorithms for optimization or graph neural networks to capture complex relationships between users, items, and criteria. These systems are increasingly important in diverse applications like e-commerce and education, offering more personalized and relevant recommendations by leveraging richer data than traditional methods.